This repository contains the code to perform Bayesian parameter estimation for spatial correlation models, as well as implementations of novel correlation models that account for site and path effects in addition to spatial proximity. The methodology and the novel correlation models are presented in :
Bodenmann L., Baker J. W., Stojadinovic B. (2023): "Accounting for path and site effects in spatial ground-motion correlation models using Bayesian inference". Natural Hazards Earth System Sciences, 23, 2387–2402, https://doi.org/10.5194/nhess-23-2387-2023
The folder data contains the data used to estimate the correlation model parameters, as well as some further data used in the introductory figure in the manuscript, and for the case study application.
The folder results contains the post-processed results illustrated in the manuscript.
Quick start Open the notebook introductory_example_estimation.ipynb on a hosted Jupyter notebook service (e.g., Google Colab). It explains how the models are implemented, how the parameters are estimated using MCMC and how to compute the predictive accuracy. It does not require any local python setup and you can easily customize the proposed models and perform the computations yourself.
The script main_estimate_postprocess.py performs all computations - from model estimation to post-processing - to reproduce the results presented in the paper (and stored in the folder results). These computations were performed on a high-performance computing cluster operating with Linux. The employed (mini-)conda environment is specified in the environment_numpyro.yml file. To enable efficient MCMC, this script uses the jax implementations of the models models_jax.py. We do not recommend to run this computationally expensive script on a personal computer.
Quick start Take a look at the introductory_example_application.ipynb notebook to see how the estimated models can be applied in regional risk simulations. It uses numpy implementation of the models models_numpy.py and the required packages are specified in environment_basic.yml.
The notebook figures.ipynb reproduces the figures shown in the manuscript using utility functions from utils_plotting.py.
We performed model estimation on Linux and did not test other opearting systems. To perform model estimation on your local machine, you can set up a Windows Subsystem for Linux (WSL). Then install miniconda and create an environment as conda env create -f environment_numpyro.yml.
Application of the estimated models and the plotting of the results does not require Linux and you can create a miniconda environment as conda env create -f environment_basic.yml.